The Context
Organizations managing regulated or sensitive data (customer information, research datasets, financial records) often work with external partners who need data access for collaboration, analysis, or compliance verification. Internal data platforms work well for internal teams, but external sharing introduces friction. Traditional sharing methods are either too insecure or too cumbersome, creating a gap between having good data and being able to share it safely.
The Challenge It Addresses
Extract-and-transfer is risky. Organizations typically extract datasets, apply basic controls, and send files or credentials to partners. This creates copies outside your control. If mishandled, you’ve lost governance. If regulations require access revocation, you can’t because data already exists elsewhere.
Manual processes are error-prone. Each sharing request involves multiple handoffs: request, extract, apply controls, send, track. Mistakes happen at every step. A column gets included that shouldn’t be. Version control breaks. Audit trails disappear.
Partners work with stale data. By the time data is extracted, reviewed, approved, and delivered, it’s outdated. When fresh data is needed, the entire process repeats. Real-time collaboration becomes impossible.
Governance visibility disappears. Once data leaves your system, you lose sight of partner activity. Who accessed it? What queries ran? Did they follow policies? You can’t prove sensitive data was handled appropriately.
Scaling becomes expensive and risky. Adding new partners means repeating the entire workflow. As partners multiply, operational overhead and risk surface both grow.
